Yongkai Wu

According to our database, Yongkai Wu authored at least 38 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling.

Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen

Trans. Mach. Learn. Res., 2024

AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab.

Ebuka Okpala, Nishant Vishwamitra, Keyan Guo, Song Liao, Long Cheng, Hongxin Hu, Yongkai Wu, Xiaohong Yuan, Jeannette Wade, Sajad Khorsandroo

CoRR, 2024

SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning.

Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li

CoRR, 2024

Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach.

Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu

Proceedings of the 2024 SIAM International Conference on Data Mining, 2024

SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models.

Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai Li, Yiran Chen

Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024

Achieving Fairness through Constrained Recourse.

Shuang Wang, Yongkai Wu

Proceedings of the International Joint Conference on Neural Networks, 2024

Achieving Equalized Explainability Through Data Reconstruction.

Shuang Wang, Yongkai Wu

Proceedings of the International Joint Conference on Neural Networks, 2024

Fair Weak-Supervised Learning: A Multiple-Instance Learning Approach.

Yucong Dai, Xiangyu Jiang, Yaowei Hu, Lu Zhang, Yongkai Wu

Proceedings of the International Joint Conference on Neural Networks, 2024

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms.

Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Long-Term Fair Decision Making through Deep Generative Models.

Yaowei Hu, Yongkai Wu, Lu Zhang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective.

Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, Yongkai Wu

CoRR, 2023

Algorithmic Recourse for Anomaly Detection in Multivariate Time Series.

Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan

CoRR, 2023

Achieving Counterfactual Fairness for Anomaly Detection.

Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

Fair Selection through Kernel Density Estimation.

Xiangyu Jiang, Yucong Dai, Yongkai Wu

Proceedings of the International Joint Conference on Neural Networks, 2023

Neural Time-Invariant Causal Discovery from Time Series Data.

Saima Absar, Yongkai Wu, Lu Zhang

Proceedings of the International Joint Conference on Neural Networks, 2023

Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision.

Feng Luo, Ling Liu, G. Geoff Wang, Vijay Kumar, Mark S. Ashton, Jacob D. Abernethy, Fatemeh Afghah, Matthew H. E. M. Browning, David Coyle, Philip M. Dames, Tom O'Halloran, James Hays, Patrick Hiesl, Chenfanfu Jiang, Puskar Khanal, Venkat Narayan Krovi, Sara Kuebbing, Nianyi Li, JingJing Liang, Ninghao Liu, Steve McNulty, Christopher M. Oswalt, Neil Pederson, Demetri Terzopoulos, Christopher W. Woodall, Yongkai Wu, Jian Yang, Yin Yang, Liang Zhao

Proceedings of the 5th IEEE International Conference on Cognitive Machine Intelligence, 2023

On Root Cause Localization and Anomaly Mitigation through Causal Inference.

Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

2022

On Interpretable Anomaly Detection Using Causal Algorithmic Recourse.

Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan

CoRR, 2022

SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge.

Aneesh Komanduri, Yongkai Wu, Wen Huang, Feng Chen, Xintao Wu

Proceedings of the IEEE International Conference on Big Data, 2022

Fair Collective Classification in Networked Data.

Karuna Bhaila, Yongkai Wu, Xintao Wu

Proceedings of the IEEE International Conference on Big Data, 2022

2021

A Generative Adversarial Framework for Bounding Confounded Causal Effects.

Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Fairness through Equality of Effort.

Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

Multi-cause Discrimination Analysis Using Potential Outcomes.

Wen Huang, Yongkai Wu, Xintao Wu

Proceedings of the Social, Cultural, and Behavioral Modeling, 2020

Fair Multiple Decision Making Through Soft Interventions.

Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms.

Lu Zhang, Yongkai Wu, Xintao Wu

IEEE Trans. Knowl. Data Eng., 2019

On Convexity and Bounds of Fairness-aware Classification.

Yongkai Wu, Lu Zhang, Xintao Wu

Proceedings of the World Wide Web Conference, 2019

PC-Fairness: A Unified Framework for Measuring Causality-based Fairness.

Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Achieving Causal Fairness through Generative Adversarial Networks.

Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Counterfactual Fairness: Unidentification, Bound and Algorithm.

Yongkai Wu, Lu Zhang, Xintao Wu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Fairness-aware Classification: Criterion, Convexity, and Bounds.

Yongkai Wu, Lu Zhang, Xintao Wu

CoRR, 2018

On Discrimination Discovery and Removal in Ranked Data using Causal Graph.

Yongkai Wu, Lu Zhang, Xintao Wu

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Achieving Non-Discrimination in Prediction.

Lu Zhang, Yongkai Wu, Xintao Wu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017

DPWeka: Achieving Differential Privacy in WEKA.

Srinidhi Katla, Depeng Xu, Yongkai Wu, Qiuping Pan, Xintao Wu

Proceedings of the IEEE Symposium on Privacy-Aware Computing, 2017

Achieving Non-Discrimination in Data Release.

Lu Zhang, Yongkai Wu, Xintao Wu

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

A Causal Framework for Discovering and Removing Direct and Indirect Discrimination.

Lu Zhang, Yongkai Wu, Xintao Wu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016

On Discrimination Discovery Using Causal Networks.

Lu Zhang, Yongkai Wu, Xintao Wu

Proceedings of the Social, Cultural, and Behavioral Modeling, 9th International Conference, 2016

Situation Testing-Based Discrimination Discovery: A Causal Inference Approach.

Lu Zhang, Yongkai Wu, Xintao Wu

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Using Loglinear Model for Discrimination Discovery and Prevention.

Yongkai Wu, Xintao Wu

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016